Project Playground

Load Packages

library(dplyr)

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Attaching package: ‘dplyr’

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    filter, lag

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library(tidyverse)

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v tibble  3.1.5     v stringr 1.4.0
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v readr   2.0.2     
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library(ggplot2)
library(janitor)

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library(lubridate)

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library(tidymodels)

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library(httr)

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library(jsonlite)

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library(sf)

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library(tmap)

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Load Data

raw_sr_data <- read.csv("SR_data.csv") %>%
  clean_names()

Just ADK

raw_adk_data <- raw_sr_data %>%
  filter(incident_adirondack_park == "true")

All Data Map

raw_sr_map <- raw_sr_data[complete.cases(raw_sr_data), ] %>%
  # select(location_found_latitude,location_found_longitude) %>%
st_as_sf(coords = c("location_found_longitude", "location_found_latitude"), crs = 4326)
tmap_mode("view")

tmap mode set to interactive viewing

tm_shape(raw_sr_map) +
  # tm_borders() +
  # tm_shape(adk_geom_data) +
  tm_dots(size=0.02,col="red", alpha = 0.5) + tm_legend(outside = TRUE) #tm_text("Locations of Incidents in New York State that Required Ranger Assistance")

Take me on a date

timey_adk_data <- na.omit(raw_adk_data) %>%
  mutate(true_start_date = paste(incident_start_date,"",format(strptime(incident_start_time, "%I:%M %p"), "%H:%M:00"))) %>%
  mutate(true_closed_date = paste(incident_closed_date,"",format(strptime(incident_closed_time, "%I:%M %p"), "%H:%M:00"))) %>%

mutate(actual_start = mdy_hms(true_start_date)) %>%
mutate(actual_closed = mdy_hms(true_closed_date)) %>%
  mutate(duration = (actual_closed-actual_start))

Not ADK

raw_not_adk_data <- raw_sr_data %>%
  filter(incident_adirondack_park == "false")

How many single people needed rescue

each_incident_num <- table(raw_adk_data$incident_number) %>%
  as.data.frame() %>%
  filter(Freq == 1 )

Counts

count_gender <-  table(raw_adk_data['subject_gender'])
View(count_gender)
count_ages <- table(raw_adk_data['subject_age'])
View(count_ages)
count_rangers <- table(raw_adk_data['number_of_rangers_involved'])
View(count_rangers)
count_elevation <- table(raw_adk_data['location_found_elevation'])
View(count_elevation)

mean(raw_adk_data$subject_age, na.rm = TRUE)

[1] 37.64492

mean(raw_not_adk_data$subject_age, na.rm = TRUE)

[1] 36.93668

Preliminary Plotting

plot_relevant <- raw_adk_data %>%
  ggplot(aes(subject_age,location_found_elevation)) + geom_point()

MAPS!

ADK Region

Relevant Data

adk_geom_data <- raw_adk_data[complete.cases(raw_adk_data), ] %>%
  # select(location_found_latitude,location_found_longitude) %>%
st_as_sf(coords = c("location_found_longitude", "location_found_latitude"), crs = 4326) 
# adk_geom_data <- left_join(adk_geom_data,raw_adk_data[complete.cases(raw_adk_data), ],by=c("code")) 

tmap_mode("view")

tmap mode set to interactive viewing

tm_shape(adk_geom_data) +
  # tm_borders() +
  # tm_shape(adk_geom_data) +
  tm_dots(size=0.02,col="subject_age", alpha = 0.7, palette = "Spectral")

NA